Reaching with altered visual feedback of the hand’s position in a virtual environment leads to reach adaptation in the trained hand, and also in the untrained hand (Wang & Sainburg, 2002). In the current study, we asked if reach adaptation in the untrained (right) hand is due to transfer of explicit (i.e., EA; conscious strategy) and/or implicit adaptation (i.e., IA; unconscious) from the left (trained) hand, and if the transfer of EA and IA changes depending on how one is made aware of the visuomotor distortion. We further asked if EA and IA are retained in the trained and untrained hand for 24 hours. Participants (n=60) were evenly divided into 3 groups (Strategy, No-Strategy, and Control). All participants reached to visual targets while seeing a cursor on the screen that was rotated 40° clockwise relative to their hand motion. Participants in the Strategy group were instructed on how to counteract the visuomotor distortion. The No-Strategy group was not told of the upcoming visuomotor distortion but was later asked to reach while engaging in any strategy they had learned in order to assess EA. Participants in the Control group were also not told of the upcoming visuomotor distortion and were never instructed to engage in any strategy when reaching. EA and IA were assessed in both the trained and untrained hands immediately following rotated reach training, and 24 hours later by having participants reach without the cursor when instructed to: (1) aim so that your hand lands on the target (to assess IA) and (2) use what was learned during training so that the cursor lands on the target (to assess EA + IA; exception of Control group). Results revealed that the groups differed with respect to the extent of reach adaptation achieved when initially training with the rotated cursor, such that the Strategy group had greater EA and less IA compared to the No-Strategy group in the trained hand. Unexpectedly, the Control group also showed less IA compared to the No-Strategy group, but was similar to the Strategy group. For both the Strategy and No-Strategy groups, EA was transferred between hands and was retained over time. While the extent of IA varied between groups in the trained (left) hand immediately following reach training trials, significant transfer of IA was not found in any of the three groups. Retention of IA was observed in the trained hand but decayed over testing days. Together, these results suggest that while initial EA and IA in the trained hand is dependent on how one is made aware of the visuomotor distortion, transfer and retention of visuomotor adaptation is driven almost exclusively by EA, regardless of instructions provided.

Visuomotor adaptation arises when reaching in an altered visual environment, where one’s seen hand position does not match their felt (i.e., proprioceptive) hand position in space. Here, we investigated if proprioceptive training (PT) benefits visuomotor adaptation, and if these benefits arise due to implicit (unconscious) or explicit (conscious strategy) processes. A total of 72 participants were divided equally into 3 groups: Proprioceptive training with feedback (PTWF), Proprioceptive training no feedback (PTNF), and Control (CTRL). The PTWF and PTNF groups completed proprioceptive training (PT), where a participant’s hand was passively moved to an unknown reference location and they judged the felt position of their unseen hand relative to their body midline on every trial. The PTWF group received verbal feedback with respect to their response accuracy on the middle 60% of trials. The CTRL group did not complete PT and instead sat quietly during this time. Following PT or time delay, all three groups reached when seeing a cursor that was rotated 30° clockwise relative to their hand motion, followed by a series of no-cursor reaches to assess implicit and explicit adaptation. Results indicated that the PTWF group improved their sense of felt hand position following PT. However, this improved proprioceptive acuity did not benefit visuomotor adaptation, as all three groups showed similar visuomotor adaptation across rotated reach training trials. Visuomotor adaptation arose implicitly, with minimal explicit contribution for all three groups. Thus, these results suggest that passive proprioceptive training with feedback does not benefit, nor hinder, implicit visuomotor adaptation.

From a parent guiding their toddler when learning to brush their teeth to a physical therapist assisting a client with their range of motion, physically interacting with other people is ubiquitous in our daily life. While some researchers have shown that haptic human-human interaction benefits performance during training as well as later individual performance (Takagi et al. 2017), others have failed to replicate these benefits (Beckers et al. 2018). Participants in these interaction groups were not aware they were haptically linked to a partner and each participant had independent control over their own virtual cursor when tracking the target. Yet, we are typically aware when we are interacting with others and often do so with tasks where we have shared control over the same control point (e.g., a toothbrush). Here, we tested the effectiveness of training alone versus training with a virtual partner when individuals were made aware of their interaction in a redundant reaching task. Participants (N = 100) completed 50 baseline trials followed by 200 trials with a clockwise cursor rotation in one of four randomly assigned groups. Two of the groups performed the adaptation trials with a virtual partner that represented either the fast (Human + Fast Agent Group) or slow (Human + Slow Agent Group) state of the two-state model (Smith et al. 2006) with 30-deg rotation. The two remaining groups performed the task alone with either the 30-deg rotation (Solo full rotation) or a 15-deg rotation (Solo half rotation). Results showed that participants in the fast agent group contributed less to correcting the rotational error early in the adaptation block, but were responsible for most of the correction later in this block, with performance most similar to the solo full rotation group. Conversely, participants in the slow agent group corrected for a greater proportion of the initial errors, but their contribution began to drift during adaptation, with performance resembling that of the solo half rotation group. This pattern of results were consistent with our theory-driven simulations. / Thesis / Master of Science (MSc) / Working with a physiotherapist is the gold-standard in rehabilitating many injuries, but this can be very time consuming and repetitive in nature. This makes it worthwhile to explore other alternatives to supplement standard rehabilitation, such as working with a virtual partner. In our experiment, we tested two partners based on human models. Participants were paired with one of the virtual partners and had to reach a target using a handle, adjusting their reach to a rotation. The partners differ in how fast they help the participant adjust for the rotation. It was found that those who completed the task with a fast-learning partner corrected less error initially and more later on, while those with the slow-learning partner corrected more error initially and less later on. These results suggest that we can influence participant behaviour with different virtual partners.

Proprioception is recalibrated following reaches with misaligned visual feedback of the hand, such that one’s sense of felt hand position is shifted in the direction of the visual feedback provided (Cressman & Henriques 2009). In the current experiment, we examined the ability of proprioceptive recalibration to be retained over an extended period of time (i.e. 4 days), and the benefits of additional training on retention in the form of recall and savings (i.e. faster re-learning on subsequent testing days). Twenty-four participants trained to reach to a target while seeing a cursor that was rotated 30° clockwise relative to their hand on an initial day of testing. Half of the participants then completed additional reach training trials on 4 subsequent testing days (Training group), whereas the second half of participants did not complete additional training (Non-Training group). Participants provided estimates of their felt hand position on all 5 testing days to establish retention of proprioceptive recalibration. Results revealed that proprioceptive recalibration was recalled 24 hours after initial training and that there was no benefit of additional training. Retention in the form of savings was observed on all days for the Training group and on Day 5 in the Non-Training group. These results reveal that proprioceptive recalibration does not benefit from additional training but is retained in the form of recall and savings. Taken together, results from the two groups of participants showed that the sensory system’s ability to change over time appeared to saturate early on, within two days of training. Moreover, the different time scales (i.e. 1 day for recall versus 4 days for savings), suggested that distinct processes may underlie recall and savings of proprioceptive recalibration.

The Process Dissociation Procedure (PDP) and Verbal Report Framework (VRF) have demonstrated that both explicit (Explicit Adaptation, EA) and implicit processes (Implicit Adaptation, IA) contribute to visuomotor adaptation. However, the definition of EA is inconsistent across the two paradigms, such that the PDP refers to EA as reflecting one’s knowledge regarding how they have to reach in the novel visuomotor environment, while the VRF refers to EA as reflecting pre-planned aiming strategies. The objective of the current experiment was to compare EA as assessed via the PDP and VRF and hence provide insight into if they are assessing similar explicit processes. Sixty-one participants were evenly divided into three groups (PDP, VRF and VRF-No Cursor) and trained to reach in a virtual environment with an aligned cursor (1 block of 45 trials) and then a cursor rotated 40° clockwise (CW) relative to hand motion (3 blocks of 45 trials). EA and IA were assessed immediately following each block of rotated reach training trials, and again 5-minutes later. In the assessment trials, the PDP group reached while using any learned strategy (EA+IA), or while not engaging in a strategy (IA) and the VRF group reported their planned aiming direction by picking a number from an array of numbers surrounding the target (EA), before reaching to the target (EA+IA) with visual feedback. The VRF-No Cursor group completed the same assessment trials as the VRF group, but no visual feedback was presented during assessment of EA and IA. Following this, participants completed a post-experiment questionnaire and a drawing task to assess their awareness of the visuomotor rotation and changes in their reaches respectively. We found that all groups adapted their reaches to the 40° CW cursor rotation. As well, averaged across participants, the magnitude and retention of EA and IA were similar between the PDP and VRF groups. However, the magnitude of EA established via the VRF was not related to participants’ post-experiment awareness of the visuomotor distortion and how they had changed their reaches, as observed in the PDP and VRF No-Cursor groups. Together, these results indicate that, while the PDP and VRF suggest similar contributions of EA and IA to visuomotor adaptation, the methods of assessment engage different explicit processes. EA assessed within the VRF does not reflect one’s awareness of the visuomotor distortion at the end of the experiment or how they changed their reaches.

To date, mental fatigue has been shown to lead to a general decline in cognitive processing and motor performance. The goal of the current research was to establish the impact of mental fatigue on the contributions of explicit (i.e., conscious strategy) and implicit (unconscious) processes to visuomotor adaptation. Participants were divided into Mental Fatigue (MF) and Control groups. Mental fatigue was induced through a time load dual back task (TLDB), in which participants were required to respond as quickly and accurately as possible to letters based on recall of previously presented letters, as well as digits displayed on the screen in a choice reaction time task. The TLDB task lasted for 32 minutes, and the Control group watched a documentary for a similar length of time. Subjective feelings of mental fatigue, as indicated on a self-report scale, demonstrated that mental fatigue was significantly higher for the MF group after completion of the TLDB task. There was no similar increase in mental fatigue for the Control group. The increased mental fatigue was associated with decreased visuomotor adaptation to a 40° cursor rotation and retention of visuomotor adaptation. In particular, participants in the MF group adapted their reaches to a lesser extent early in training compared to the Control group and demonstrated less retention of visuomotor adaptation following a 20-minute rest. Furthermore, correlational analyses established that greater mental fatigue reported by participants in the MF group was associated with less explicit adaptation and greater implicit adaptation. Taken together, these results suggest that mental fatigue decreases the ability to engage in explicit processing, limiting the overall extent of visuomotor adaptation achieved.

abstract: Adapting to one novel condition of a motor task has been shown to generalize to other naïve conditions (i.e., motor generalization). In contrast, learning one task affects the proficiency of another task that is altogether different (i.e. motor transfer). Much more is known about motor generalization than about motor transfer, despite of decades of behavioral evidence. Moreover, motor generalization is studied as a probe to understanding how movements in any novel situations are affected by previous experiences. Thus, one could assume that mechanisms underlying transfer from trained to untrained tasks may be same as the ones known to be underlying motor generalization. However, the direct relationship between transfer and generalization has not yet been shown, thereby limiting the assumption that transfer and generalization rely on the same mechanisms. The purpose of this study was to test whether there is a relationship between motor generalization and motor transfer. To date, ten healthy young adult subjects were scored on their motor generalization ability and motor transfer ability on various upper extremity tasks. Although our current sample size is too small to clearly identify whether there is a relationship between generalization and transfer, Pearson product-moment correlation results and a priori power analysis suggest that a significant relationship will be observed with an increased sample size by 30%. If so, this would suggest that the mechanisms of transfer may be similar to those of motor generalization. / Dissertation/Thesis / Masters Thesis Biomedical Engineering 2018

Healthy individuals can adjust their movements when changes arise to the body or the environment. Advanced age is associated with central and peripheral changes that may negatively impact one’s ability to adapt motor performance, such us upper-limb (UL) reaching movements. In this thesis, we conducted four studies to address the impact of aging on coordination and adaptation of goal-directed reaches. In the first experiment, we examined compensatory arm–trunk coordination in older adults during trunk-assisted reaching, using two motor tasks : 1) the Stationary Hand Task (SHT) in which older and young participants were asked to maintain a fixed hand position while flexing forward at the trunk, and 2) the Reaching Hand Task (RHT) in which participants were instructed to reach to a within-arm’s reach target while simultaneously flexing forward at the trunk (Raptis et al., 2007; Sibindi et al., 2013). We found that in SHT, young and older participants were able to maintain a stable hand position and compensate for trunk movement by appropriate angular rotations at the elbow and shoulder joints. As well, in the RHT, both groups made similar small overshoot errors. However, older participants performance was significantly more variable compared to young adults. These results suggest that older adult preserve their ability to coordinate arm and trunk movements efficiently during reaching but are not as consistent as young adults. In the second experiment, we sought to determine the ability of older adults to adjust shoulder and elbow coordination in response to changing task demands. Thus, we asked young and older adults to perform the RHT of Raptis et al. (2007) from the first experiment. A detailed comparison of UL kinematics during reaches in the presence and absence of trunk motion (i.e., free- vs. blocked-trunk trials) was performed and compared between young and older adults. We found that participants in both age group were able to coordinate motion at the elbow and shoulder joints in accordance with motion at the trunk. However, the extent of changes at the UL joints was smaller and more variable in older adults compared to young ones, especially when trunk motion was involved. These results imply that older adults can coordinate their UL movements based on task requirements, but with less consistent performance compared to young adults. In the third experiment, we investigated the preservation of intermanual transfer and retention of implicit visuomotor adaptation in older adults. We had young and older participants train to reach with visual feedback rotated 30° counter-clockwise relative to their actual hand motion. Furthermore, we examined whether providing augmented somatosensory feedback regarding movement endpoint would enhance visuomotor adaptation. We found that older adults demonstrated a comparable magnitude of implicit adaptation, transfer, and retention of visuomotor adaptation as observed in young adults, regardless of the presence of augmented somatosensory feedback. These results indicate that intermanual transfer and retention do not differ significantly between young and older adults when adaptation is driven implicitly, regardless the availability of augmented somatosensory feedback. In the fourth experiment, we looked to determine age-related differences in the engagement of offline and online control processes during implicit visuomotor adaptation. A detailed analysis of reaching performance was conducted and between young and older adults, during and after visuomotor adaptation. We found that when rotation was introduced, participants in both age took longer time to complete their movements, reached with a lower peak velocity and spent more time homing in on the target compared to reaches with aligned cursor feedback. Additionally, older adults had more curved paths with rotated cursor feedback compared to their reaches with aligned cursor feedback. Moreover, these changes in reaching performance continued following adaptation for both groups. These results suggest that young and older adults engage more in online control processes during implicit visuomotor adaptation. Together, these studies show that older adults: 1) maintain the ability to use compensatory arm-trunk coordination to maintain reaching accuracy, 2) preserve the ability to adjust the coordination between UL joints to meet task demands, 3) maintain the ability to adjust reaches to meet changes in the reaching environment, as well as transfer and retain the newly acquired movement, and 4) preserve the ability to modify the control processes underlying these adapted movements to meet the demands of the reaching environment. In conclusion, the flexibility to coordinate and adapt upper limb reaching performance to meet changes in task demands is maintained across lifespan.

Essential tremor (ET) is the most common movement disorder worldwide and is characterized by an isolated tremor of the upper limb that worsens over the course of time. Evidence has accumulated to support the theory that the cerebellum is primary involved in the development of ET disease, although the contribution of cerebellar pathology to ET’s aetiology remains poorly understood. Beside motor deficits, numerous studies report the presence of cognitive impairment in ET patients. The cerebellum is crucial for motor as well as cognitive functions as it integrates sensorimotor information to create an internal model of movement using prediction errors. In this study, I tested the performance of 34 ET patients and 34 age-matched healthy controls in a visuomotor adaptation (VMA) task whose proper execution critically depends on the cerebellum. Participants performed the VMA while sitting in front of a computer screen. At the beginning of each trial, eight grey circles in one of eight possible positions arrayed around a central cross appeared on the screen. Next, one of the eight circles was marked as a blue target, and participants had to move from the central cross towards the target using a digital pen moved on a digital tablet. The movement on the tablet was represented as a cursor on the screen. Visual feedback from the moving hand was prevented. Over the course of the experiment, a 30° clockwise visuomotor perturbation of the cursor movement on the screen was introduced abruptly. To this end, subjects implicitly modified the reach direction such that they are able to hit the target again. The extent to which a subject adapts to the visuomotor perturbation can be measured by the angular error between a straight line connecting the center cross and the target, and a line connecting the center cross and the position of the cursor at peak velocity. Reaction times and movement times were analyzed to assess motor performance. In accordance with my hypothesis, I found evidence for impaired visuomotor adaptation in ET that could not be explained by altered general motor performance due to tremor. This deficit was also specific to both early and late adaptation phases. There were no group differences during a baseline phase, in which no visual perturbation was present, as well as at a de-adaptation phase, when the visual perturbation was suddenly removed. This deficit seems to also not relate to clinical features, i.e., disease state (measured by TETRAS/SARA), disease duration, current medication, and patients’ cognitive state (evaluated by MoCA). Thus, these findings support the hypothesis that a functional disturbance of the cerebellum is present in mildly to moderately affected ET patients without marked cerebellar signs and is detectable using a behavioral task that targets cerebellar functionality. What could be further mechanisms that negatively affect visuomotor adaptation in patients with ET and are not associated with basic motor functions? Unlike a pure motor task, the visuomotor adaptation task entails a cognitive component with implicit and/or explicit learning processes. Thus, it could be that cognitive deficits in ET, frequently reported among studies may have driven performance deficits in this task. Note however that I did not find any association between cognitive abilities as measured by MoCA and visuomotor adaptation impairment in the ET cohort. As no extensive neurocognitive testing was performed in our cohort and MoCA was shown to be not very sensitive for cerebellar cognitive symptoms, it is impossible to rule out the effect of cognitive decline in ET on visuomotor adaptation.

Human movements are remarkably adaptive, such that we are capable of completing movements in a novel environment with similar accuracy to those performed in a typical environment. Our ability to perform in these environments involves accurate processing of sensory feedback for online and offline control. These processes of control have been widely studied for well learned actions, but not for actions in a novel visuomotor environment. In two experiments, we examined control processes underlying reaches when participants were first introduced to a visuomotor rotation (Experiment 1) and then following visuomotor adaptation (Experiment 2). All participants completed 150 reach training trials when (1) a cursor accurately represented their hand motion (i.e., aligned cursor) and (2) a cursor was rotated 45 degrees clockwise relative to their hand motion (i.e., rotated cursor). In Experiment 1, we sought to determine if the control processes underlying movements in typical and novel visuomotor conditions were comparable. Participants (n = 16) received either continuous visual feedback or terminal visual feedback regarding movement endpoint during reach training. Analyses revealed that participants were able to demonstrate similar outcomes (i.e., movement time and endpoint errors) regardless of visual or cursor feedback, but also demonstrated more offline control (i.e., took more time planning and were less consistent in initiating their movements) when reaching with a rotated cursor compared to an aligned cursor, even at the end of training. Together, the results suggest a greater contribution of offline control processes and less effective online corrective processes when reaching in a novel environment compared to when reaching in a typical environment. In attempt to promote online corrective processes, participants (n = 16) in Experiment 2 first completed the training trials with continuous visual feedback and then completed an additional 45 reaches under (1) slow movement time (i.e., Slow MT: 800-1000 ms) and (2) fast movement time (i.e., Fast MT: 400-500ms) constraints. Results showed a shift to online control (i.e., greater endpoint accuracy) when reaching with an aligned and rotated cursor, when sufficiently more time was provided (i.e., Slow MT). Specifically, participants were able to more effectively utilize visual feedback for online control under the Slow MT constraint compared to when reaching quickly (i.e., Fast MT). Together, these experiments demonstrate a flexibility in control processes underlying reaches with rotated visual feedback of the hand. In that reaches first engage in offline control processes during adaptation to a visuomotor rotation, and then shift to online corrective processes following visuomotor adaptation.